Method and apparatus with feature embedding

ABSTRACT

A method and apparatus with feature embedding is provided. The method includes estimating a depth map for each of plural two-dimensional (2D) input images, transforming the depth maps into three-dimensional (3D) information in a point cloud form based on an aggregation of the depth maps, and generating an embedded feature by applying the 3D information to a machine learning model, where the embedded feature includes information about a 3D shape corresponding to a 2D object in the plural 2D input images.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC § 119(a) of KoreanPatent Application No. 10-2020-0121531, filed on Sep. 21, 2020, in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to a method and apparatus with featureembedding.

2. Description of Related Art

In a typical approach, an object included in an image with an individualoriginal pixel intensity may be identified by extracting alow-dimensional feature vector of the image through a neural network.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, a processor-implemented method includesestimating a depth map for each of plural two-dimensional (2D) inputimages, transforming the depth maps into three-dimensional (3D)information in a point cloud form based on an aggregation of the depthmaps, and generating an embedded feature by applying the 3D informationto a machine learning model, where the embedded feature includesinformation about a 3D shape corresponding to a 2D object in the plural2D input images.

The transforming may include incrementally aggregating the depth maps,and transforming a result of the incremental aggregating into the 3Dinformation.

The transforming may include unprojecting the depth maps to a 3D spaceusing a camera parameter corresponding to the plural 2D input images.

The transforming may include unprojecting the depth maps to the 3D spaceto calculate positions of 3D points that correspond to pixelscorresponding to the 2D object in the plural 2D input images, andtransforming the depth maps into the 3D information based on thecalculated positions of the 3D points.

The plural 2D input images may be multi-view images or a sequence image.

The plural 2D input images may be multi-view images, and the 3Dinformation may include information indicating respective surfaces ofthe 3D shape corresponding to respective views of the plural 2D inputimages.

The machine learning model may be a neural network, and the generatingof the embedded feature may include transforming the 3D information intoinformation of a dimension corresponding to an input layer of the neuralnetwork, generating the embedded feature by applying the information ofthe dimension to the neural network.

The transforming of the 3D information into the information of thedimension may include transforming the 3D information into theinformation of the dimension using at least one of a multilayerperceptron (MLP) and a graph convolutional network (GCN).

The machine learning model may be a neural network, and the generatingof the embedded feature may include generating the embedded feature toinclude information about the 3D shape, representing depth values of allpixels corresponding to the 2D object included in the plural 2D inputimages, by applying the 3D information to the neural network.

The embedded feature may be in a form of one of a feature map and afeature vector.

The method may further include reconstructing the 2D object as a 3Dobject based on the embedded feature.

The reconstructing of the 2D object as the 3D object may includeestimating a probability that a display pixel corresponding to theembedded feature is located inside or outside the 3D shape, andreconstructing the 2D object as the 3D object based on the estimatedprobability.

The estimating of the depth map for each of the plural 2D input imagesmay include estimating respective depth maps from each of the plural 2Dinput images using a neural network that is trained to estimate depth.

The method may further include obtaining respective intrinsic andextrinsic parameters corresponding to the plural 2D input images, andperforming the transforming dependent on the obtained respectiveintrinsic and extrinsic parameters.

In one general aspect, one or more embodiments may include anon-transitory computer-readable storage medium storing instructionsthat, when executed by a processor, cause the processor to perform oneor more or all operations, processes, or methods described herein.

In one general aspect, an apparatus includes a processor configured toestimate a depth map for each of plural two-dimensional (2D) inputimages, transform the depth maps into three-dimensional (3D) informationin a point cloud form based on an aggregation of the depth maps, andgenerate an embedded feature by applying the 3D information to a machinelearning model, were the embedded feature includes information about a3D shape corresponding to a 2D object included in the plural 2D inputimages.

For the transforming, the processor may be configured to incrementallyaggregate the depth maps and transform a result of the incrementalaggregation into the 3D information.

For the transforming, the processor may be configured to unproject thedepth maps to a 3D space using a camera parameter corresponding to theplural 2D input images.

The processor may be configured to unproject the depth maps to the 3Dspace to calculate positions of 3D points that correspond to pixelscorresponding to the 2D object in the plural 2D input images, transformthe depth maps into the 3D information based on the calculated positionsof the 3D points.

The plural 2D input images may be multi-view images or a sequence image.

The plural 2D input images may be multi-view images, and the 3Dinformation may include information indicating respective surfaces ofthe 3D shape corresponding to respective views of the plural 2D inputimages.

The machine learning model may be a neural network, and, for thegenerating of the embedded feature, the processor may be configured totransform the 3D information into information of a dimensioncorresponding to an input layer of the neural network, and generate theembedded feature by applying the information of the dimension to theneural network.

For the transforming of the 3D information into the information of thedimension, the processor may be configured to transform the 3Dinformation into the information of the dimension using at least one ofa multilayer perceptron (MLP) and a graph convolutional network (GCN).

The machine learning model may be a neural network, and, for thegenerating of the embedded feature, the processor may be configured togenerate the embedded feature to include information about the 3D shape,representing depth values of all pixels corresponding to the 2D objectincluded in the plural 2D input images, by applying the 3D informationto the neural network.

The embedded feature may be in a form of one of a feature map and afeature vector.

The processor may be configured to reconstruct the 2D object as a 3Dobject based on the embedded feature.

For the reconstructing of the 2D object as the 3D object, the processormay be configured to estimate a probability that a display pixelcorresponding to the embedded feature is located inside or outside the3D shape, and reconstruct the 2D object as the 3D object based on theestimated probability.

For the estimating of the depth map for each of the plural 2D inputimages, the processor may be configured to estimate respective depthmaps from each of the plural 2D input images using a neural network thatis trained to estimate depth.

The apparatus may further include an interface configured to obtain theplural 2D input images, where the processor may be configured performthe transforming of the depth maps dependent on respective intrinsic andextrinsic parameters corresponding to the obtaining of the plural 2Dinput images.

The interface may be a communication interface.

The apparatus may be one of a 3D printer, a 3D scanner, an advanceddriver-assistance system (ADAS), a head-up display (HUD), a 3D digitalinformation display (DID), a navigation device, a neuromorphic device, a3D mobile device, a smartphone, a smart television (TV), a smartvehicle, an Internet of Things (IoT) device, a medical device, and ameasuring device.

The processor may be further configured to reconstruct the 2D object asa 3D object, based on the embedded feature, and the apparatus may be anaugmented reality apparatus and further include a display controlled todisplay the reconstructed 2D object.

The apparatus may further include a memory, and the processor may befurther configured to store the embedded feature in the memory.

In one general aspect, an augmented reality (AR) apparatus includes acommunication interface configured to receive plural two-dimensional(2D) input images including multi-view images or a sequence image, aprocessor configured to estimate respective depth maps for the plural 2Dinput images, transform the respective depth maps into three-dimensional(3D) information in a point cloud form based on an aggregation of therespective depth maps, perform encoding of a feature to includeinformation about a 3D shape corresponding to a 2D object included inthe plural 2D input images by applying the 3D information to a neuralnetwork, and reconstruct the 3D shape corresponding to the 2D objectbased on the feature, and further include a display configured todisplay an output image including the 3D shape.

The apparatus may further include a memory, and the processor may befurther configured to store the feature in the memory.

In one general aspect, an apparatus includes a memory storing anembedded feature that includes information about a 3D shapecorresponding to a 2D object, a display, and a processor configured toreconstruct the 2D object as a 3D object based on the embedded featureand control display of the reconstructed 2D object, where the embeddedfeature is a feature reflecting having been generated based on depthmaps for each of plural two-dimensional (2D) images, a transformation ofthe depth maps into three-dimensional (3D) information based on anaggregation of the depth maps, and by an application of the 3Dinformation to a neural network.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 illustrate examples of feature embedding processes.

FIG. 3 illustrates an example of an unprojection concept.

FIG. 4 illustrates an example of a process in which depth maps areincrementally aggregated.

FIG. 5 illustrates an example of a process in which depth mapscorresponding to multi-view images are incrementally aggregated.

FIG. 6 illustrates an example of a process of generating a featureincluding information about a three-dimensional (3D) shape from 3Dinformation.

FIG. 7A illustrates an example of a training of a neural network.

FIG. 7B illustrates an example of a reconstructing of a two-dimensional(2D) object into a 3D object.

FIG. 8 illustrates an example of a feature embedding method.

FIG. 9 illustrates an example of an apparatus with feature embedding.

Throughout the drawings and the detailed description, unless otherwisedescribed or provided, the same drawing reference numerals will beunderstood to refer to the same elements, features, and structures. Thedrawings may not be to scale, and the relative size, proportions, anddepiction of elements in the drawings may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein.

Further, the following detailed descriptions are possessed or acquiredby the inventor(s) in the course of conceiving the present disclosure.However, various changes, modifications, and equivalents of the methods,apparatuses, and/or systems described herein will be apparent after anunderstanding of the disclosure of this application. For example, thesequences of operations described herein are merely examples, and arenot limited to those set forth herein, but may be changed as will beapparent after an understanding of the disclosure of this application,with the exception of operations necessarily occurring in a certainorder. Also, descriptions of features that are known after anunderstanding of the present disclosure may be omitted for increasedclarity and conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided merelyto illustrate some of the many possible ways of implementing themethods, apparatuses, and/or systems described herein that will beapparent after an understanding of the disclosure of this application.

The following structural or functional descriptions of examplesdisclosed in the present disclosure are merely intended for the purposeof describing the examples and the examples may be implemented invarious forms. The examples are not meant to be limited, but it isintended that various modifications, equivalents, and alternatives arealso covered within the scope of the claims.

Although terms of “first” or “second” are used to explain variouscomponents, the components are not limited to the terms. These termsshould be used only to distinguish one component from another component.For example, a “first” component may be referred to as a “second”component, or similarly, and the “second” component may be referred toas the “first” component within the scope of the right according to theconcept of the present disclosure.

Throughout the specification, when a component is described as being“connected to,” or “coupled to” another component, it may be directly“connected to,” or “coupled to” the other component, or there may be oneor more other components intervening therebetween. In contrast, when anelement is described as being “directly connected to,” or “directlycoupled to” another element, there can be no other elements interveningtherebetween. Likewise, similar expressions, for example, “between” and“immediately between,” and “adjacent to” and “immediately adjacent to,”are also to be construed in the same way. As used herein, the term“and/or” includes any one and any combination of any two or more of theassociated listed items.

As used herein, the singular forms are intended to include the pluralforms as well, unless the context clearly indicates otherwise. Forexample, the articles “a,” “an,” and “the” are intended to include theplural forms as well, unless the context clearly indicates otherwise. Itshould be further understood that the terms “comprises”, “includes”,“has”, “comprising” “including”, and “having”, as non-limiting example,when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, components or acombination thereof, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains and basedon an understanding of the disclosure of the present application. Terms,such as those defined in commonly used dictionaries, are to beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art and the disclosure of the presentapplication, and are not to be interpreted in an idealized or overlyformal sense unless expressly so defined herein. The use of the term“may” herein with respect to an example or embodiment (e.g., as to whatan example or embodiment may include or implement) means that at leastone example or embodiment exists where such a feature is included orimplemented, while all examples are not limited thereto.

Hereinafter, examples will be described in detail with reference to theaccompanying drawings, and like reference numerals in the drawings referto like elements throughout.

As noted above, in a typical approach, an object included in an imagewith an individual original pixel intensity may be identified byextracting a low-dimensional feature vector of the image through aneural network. However, it is found that an approach may beunsatisfactory to sufficiently utilize information at each view, or toembed features by using similarity information between sequentiallyinput images, if the sequentially input images are multi-view images orotherwise sequenced images.

FIGS. 1 and 2 illustrate examples of feature embedding processes. Forexample, each of FIGS. 1 and 2 illustrate an example process in whichfeature embedding generates a feature that includes information about athree-dimensional (3D) shape corresponding to a two-dimensional (2D)object, for example, a vehicle, included in a 2D input image. An exampleapparatus herein with feature embedding may implement feature embeddingprocesses.

In operation 110, the example apparatus estimates depth maps for each 2Dinput image. The 2D input image may be, for example, multi-view imagesincluding images captured at different views, or a sequence imageincluding a plurality of image frames differentiated in time. Dependingon example, the 2D input image may also be a single image. The 2D inputimage may be an RGB image. For example, the apparatus may estimate depthmaps {D_(i)}_(i=1) ^(N) from 2D input images {I_(i)}_(i−1) ^(N) of eachview, using a neural network (for example, a depth estimation network220 of FIG. 2) that is trained to estimate a depth from an input image.In this example, i denotes a view index and N denotes a number ofimages.

The depth estimation network 220 may be, for example, a neural networkthat is trained to detect correspondence points in two or more 2D inputimages and to estimate depth maps from the multiple 2D input imagesthrough stereo matching, for example, to estimate a depth of an objectin an image. Also, the depth estimation network 220 may be trained toestimate a depth map from a 2D input image using various schemes ofobtaining depth information from a 2D image, in various examples.

In operation 120, the apparatus transforms the depth maps {D_(i)}_(i−1)^(N) estimated in operation 110 to 3D information 245 in a point cloudform by aggregating the depth maps {D_(i)}_(i=1) ^(N). For example, theapparatus may perform an unprojection 230 of the depth maps{D_(i)}_(i=1) ^(N) to a 3D space using a camera parameter {V_(i)}_(i=1)^(N) corresponding to each 2D input image, to transform the depth maps{D_(i)}_(i=1) ^(N) to the 3D information 245. The “camera parametercorresponding to each 2D input image” may be construed to be a cameraparameter {V_(i)}_(i=1) ^(N) of a camera 210 that captures each 2D inputimage.

The camera parameter {V_(i)}_(i=1) ^(N) may include one or moreintrinsic parameters and one or more extrinsic parameters. The intrinsicparameter may include, for example, a focal length (fx, fy)corresponding to a distance between an image sensor and a center of alens of a camera, a principal point (cx, cy), and a skewnesscoefficient. A focal length f may be expressed in units of pixels. Also,fx may represent how many times a size of a horizontal cell or a gapbetween horizontal cells the focal length is, and fy may represent howmany times a size of a vertical sensor cell or a gap between verticalsensor cells the focal length is. A principal point c may correspond toimage coordinates of a foot of a perpendicular line drawn from thecenter of the lens of the camera (e.g., a pinhole) to the image sensor.The skewness coefficient may be a degree by which a y axis of a cellarray of the image sensor is skewed. Also, the extrinsic parameter maybe a parameter that describes a transformation relationship between acamera coordinate system and a world coordinate system, and may berepresented by rotation and translation transformation between thecamera coordinate system and the world coordinate system, for example.In an example, the extrinsic parameter may not be a unique parameter ofthe camera and may correspond to a parameter, for example, aninstallation height of the camera or an installation direction such as apan or a tilt, associated with a geometric relationship between thecamera and an external space. Also, the extrinsic parameter may varydepending on how the world coordinate system is defined in variousexamples.

For example, in operation 120, the apparatus may obtain features informs of 3D volumes in the world coordinate system by performing theunprojection 230 of a depth map estimated from each input view imagebased on an intrinsic parameter and an extrinsic parameter of thecamera. In this example, the features in the forms of the 3D volumes maybe fused into a single volume using, for example, a convolution-basedrecurrent neural network (RNN) module or a gated recurrent unit (GRU),which may then be applied or provided to a machine learning model 250,e.g., a neural network 250.

The example neural network 250 may be, for example, a convolutionalneural network (CNN), a convolutional long-short term memory (ConvLSTM)network, a convolutional GRU (ConvGRU) network, or a neural network of arecurrent structure such as an RNN, as non-limiting examples.

The apparatus may obtain a set {X|X∈

³} of point clouds in the world coordinate system by performing theunprojection 230 of the depth maps {D_(i)}_(i=1) ^(N) to the 3D space,as shown in Equation 1 below, using a camera parameter corresponding toeach view of a corresponding 2D input image together with a depth mapestimated for each of the camera views.

X _(w) =[R ^(T) |−R ^(T) t]K ^(−1ũ)  Equation 1

In Equation 1, and as illustrated in FIG. 3 discussed further below,X_(w) denotes a position of the point cloud corresponding to a set of 3Dpoints, and K denotes a projection matrix of the camera. R denotes a 3×3rotation matrix, and t denotes a 3×1 translation vector. Also, ũ denotes[uZ_(c) vZ_(c) Z_(c)]^(T), that is, a value obtained by multiplying adepth value Z_(c) of each pixel by homogeneous coordinates [u v 1] of apixel position in a 2D input image.

An example in which the apparatus performs the unprojection 230 of thedepth maps {D_(i)}_(i=1) ^(N) to the 3D space will be further describedbelow with reference to FIG. 3.

3D information obtained from 2D input images at each view may representa respective surface of a 3D shape corresponding to a 2D object observedat a corresponding view. The apparatus may obtain a single shaperepresented in the point cloud form in the 3D space by aggregating depthmaps estimated for the 2D input images at all views. The single shaperepresented in the point cloud form may correspond to the 3D information245.

The apparatus may perform an incremental aggregation 240 of the depthmaps {D_(i)}_(i=1) ^(N) for each 2D input image and may transform thedepth maps {D_(i)}_(i=1) ^(N) into the 3D information 245.

The 3D information in the point cloud form, obtained through theabove-described process, may then be embedded as a single 3D shape orfeatures including information about the 3D shape. Herein, “embedding”may be construed as a projecting of data of one dimension to data ofanother dimension. In an example, a number of dimensions of data inputto the neural network 250 may be reduced through embedding, andaccordingly one or more examples demonstrate an increase a computationspeed and reduction in computing resources.

Additional examples of the transform the depth maps {D_(i)}_(i=1) ^(N)to the 3D information 245, by incrementally aggregating the depth maps{D_(i)}_(i=1) ^(N), will be described in greater depth further belowwith reference to FIGS. 4 and 5. The 3D information 245 may includeinformation representing an exterior, for example, surface or extent ofa 3D shape corresponding to the 2D object, for example, a vehiclepresented in the input 2D images, and thus, may also correspond to 3Dpoints corresponding to pixels corresponding to the inside of thevehicle.

In operation 130, the apparatus generates a feature X^(3D) 255 includinginformation about the 3D shape corresponding to the 2D object includedin the 2D input image {I_(i)}_(i=1) ^(N), by applying or providing the3D information 245 obtained in operation 120 to the neural network 250.The neural network 250 may be, for example, an encoder, or anautoencoder that includes an encoder portion and a decoder portion, asnon-limiting examples.

The feature X^(3D) 255 may be in a form of one of a feature map and afeature vector, however, is not necessarily limited thereto. The featureX^(3D) 255 may be expressed in various forms capable of includinginformation about a 3D shape.

The apparatus may generate the feature 255 including information aboutthe 3D shape, e.g., including depth values of all pixels presented orcorresponding to the 2D object included in the 2D input images, byapplying or providing the 3D information 245 obtained in operation 120to the neural network 250. An example in which the apparatus generates afeature by applying 3D information to such a neural network will befurther described below with reference to FIG. 6.

The apparatus may thus reconstruct the 2D object into a 3D object basedon the feature X^(3D) 255 generated in operation 130. Such an example inwhich the apparatus reconstructs a 2D object included in a 2D inputimage into a 3D object will be described in greater detail below withreference to FIG. 7B.

Through the above-described process, the apparatus may generate a novelview image that may not otherwise be observed or observable from the 2Dinput images obtained at a single view or a plurality of views. Also,the apparatus may recognize a 3D shape corresponding to the 2D objectincluded in the 2D input image(s), or may retrieve a 3D modelcorresponding to the 2D object.

FIG. 3 illustrates an example of the concept of unprojection. FIG. 3illustrates a process of obtaining an image based on a pinhole cameramodel, and illustrates a transformation relationship between pixelcoordinates 310 in 2D or the image plane, camera coordinates 330, andworld coordinates 350 in 3D.

The pinhole camera model may correspond to a model representing ageometric projection relationship between the 3D space and the 2D imageplane.

An example apparatus may calculate pixel coordinates 310 correspondingto the world coordinates 350 by projecting the world coordinates 350 toa 2D image using an extrinsic parameter [R|t] and an intrinsic parameterK in the pinhole camera model of FIG. 3. The extrinsic parameter [R|t]may correspond to a rotation/translation transformation matrix totransform the 3D world coordinate system to the camera coordinatesystem.

To obtain the world coordinates 350 corresponding to the pixelcoordinates 310 from the pixel coordinates 310, a distance, for example,a depth value, from the camera coordinates 330 to the world coordinates350 may be known or predetermined.

In an example, the apparatus may perform feature embedding by obtaininga 3D shape from a 2D input image including a 2D object based on theabove-described pinhole camera model.

For example, when a depth map is estimated from a 2D input image withthe pixel coordinates 310 using a trained neural network or a deeplearning-based algorithm, as non-limiting examples, the apparatus mayunproject the depth map to a 3D space, that is, to the world coordinates350 using the camera model parameters. Through the unprojecting, theapparatus may calculate positions of 3D points corresponding to pixelscorresponding to a 2D object included in the 2D input image. Theapparatus may transform the depth map into 3D information based on thepositions of the 3D points corresponding to the pixels corresponding tothe 2D object. In this example, the 3D information may correspond to a3D shape in the point cloud form. The apparatus, among other operations,may perform feature embedding for the 3D shape by applying 3Dinformation in the point cloud form to the neural network. Through theabove-described example process, the apparatus may obtain a new featurewith information about the 3D shape from the 2D input image.

FIG. 4 illustrates an example of a process in which depth mapscorresponding to a sequence image are incrementally aggregated. FIG. 4illustrates a 2D input image 410, and depth maps 420, 430 and 440 thatare estimated from the 2D input image 410.

In this example, the 2D input image 410 may be assumed as a sequenceimage including image frames corresponding to times t−1, t and t+1.

An example apparatus may incrementally aggregate depth maps estimatedfor each image frame included in the sequence image, and may transformthe depth maps into 3D information.

The apparatus may estimate the depth map 420 from the image framecorresponding to the time t−1, may aggregate a depth map of the time testimated from the image frame corresponding to the time t with thedepth map 420, and may obtain the depth map 430. Also, the apparatus mayaggregate a depth map of the time t+1 estimated from the image framecorresponding to the time t+1 with the depth map 430, and may obtain thedepth map 440.

Thus, the depth map 440 that is finally obtained by incrementallyaggregating depth maps for each input image may include enrichedinformation corresponding to the 2D object, for example, the exampleairplane, included in the 2D input image 410, that is, information abouta more clear shape of the airplane.

FIG. 5 illustrates an example of a process in which depth mapscorresponding to multi-view images are incrementally aggregated. FIG. 5illustrates cameras 501, 502 and 503, 3D information 510, 520 and 530,and resultant features 515, 525 and 535. The cameras 501, 502 and 503may capture 2D input images. The 3D information 510, 520 and 530 may beobtained by unprojecting depth maps estimated from each of input imagesat the different views (for example, views v_(i−1), v_(i) and v_(i+1))captured by the cameras 501, 502 and 503 to a 3D space of the worldcoordinate system. The features 515, 525 and 535 may be generated byapplying the 3D information 510, 520 and 530 to an encoder 505, forexample. The cameras 501, 502 and 503 may capture images at differentpositions, for example, the views v_(i−1), v_(i) and v_(i+1), or may befixed at the same position to capture plural images. The encoder 505 maybe configured by a PointNet or a 3D CNN based on a CNN structure.Depending on examples, the encoder 505 may embed features extracted froma plurality of consecutive images into an image.

For example, an input image of the view v_(i−1) is assumed to becaptured by the camera 501. In this example, an example apparatus, amongother operations, may transform a depth map estimated from the inputimage of the view v_(i−1) into the 3D information 510 by unprojectingthe depth map to the 3D space. The 3D information 510 may includepositions of 3D points corresponding to pixels corresponding to a 2Dobject included in the input image of the view v_(i−1).

The apparatus may generate the feature X^(3D) 515 including informationabout a 3D shape corresponding to the 2D object included in the inputimage of the view v_(i−1), by applying the 3D information 510 to theencoder 505. The feature 515 may correspond to, for example, a 3Dfeature map or a 3D vector whose size is [H_(f), W_(f), C_(f)]. H_(f),W_(f), and C_(f) may represent a height, a width, and a channel of afeature map, respectively.

When an input image of the view v_(i) is captured by the camera 502, theapparatus may aggregate the depth map estimated from the input image ofthe view v_(i−1) and a depth map estimated from the input image of theview v_(i−1) may unproject the depth maps to the 3D space, and maytransform the depth maps into the 3D information 520. The apparatus maygenerate the feature X^(3D) 525 including information about a 3D shapecorresponding to 2D objects included in the input image of the viewv_(i−1) and the input image of the view v_(i), by applying the 3Dinformation 520 to the encoder 505.

When an input image of the view v_(i+1) is captured by the camera 503,the apparatus may aggregate the depth map estimated from the input imageof the view v_(i−1), the depth map estimated from the input image of theview v_(i), and a depth map estimated from the input image of the viewv_(i+1), may unproject the depth maps to the 3D space, and may transformthe depth maps corresponding to the input image of the view v_(i−1), theinput image of the view v_(i), and the input image of the view into the3D information 530. The apparatus may generate the feature X^(3D) 535including information about a 3D shape corresponding to 2D objectsincluded in each of the input image of the view v_(i−1), the input imageof the view v_(i), and the input image of the view v_(i+1), by applyingthe 3D information 530 to the encoder 505.

For example, multi-view images such as images at the views v_(i−1),v_(i) and v_(i+1) may be transformed to single 3D geometry informationsuch as the 3D information 530, and thus it is possible to more quicklyand easily perform feature embedding of a 3D shape.

FIG. 6 illustrates an example of a process of generating a featureincluding information about a 3D shape from 3D information. FIG. 6illustrates a process in which a feature 650 includes information abouta 3D shape by applying 3D information 610 in a point cloud form obtainedthrough the above-described unprojection process to a neural network630.

An example apparatus may, among other things, transform the 3Dinformation 610 in the point cloud form obtained through theabove-described unprojection process into information of a dimensioncorresponding to an input layer of the neural network 630. For example,the apparatus may transform x, y and z coordinates of each of “N” pointsinto L-dimensional vectors, and may extract (1×L)-dimensionalinformation from (N×L)-dimensional information in which theL-dimensional vectors are accumulated. The apparatus may use amultilayer perceptron (MLP) or a graph convolutional network (GCN) totransform x, y and z coordinates of each of “N” points intoL-dimensional vectors, however, examples are not limited thereto. Inthis example, L may be greater than “3”, and the apparatus may extractthe (1×L)-dimensional information by performing max pooling of eachcolumn in the (N×L)-dimensional information.

The input layer of the neural network 630 may have a structurecorresponding to a 1×L dimension. The apparatus may generate the feature650 by applying the extracted (1×L)-dimensional information to theneural network 630.

FIG. 7A illustrates an example of a process of training a neuralnetwork. Referring to FIG. 7A, an example apparatus aggregates depthmaps estimated from a single input image or multiple input images usinga trained depth estimation network, performs an unprojection 230 of thedepth maps to a 3D space, and obtains 3D information 245. The apparatusmay transform the 3D information 245 using the above-described scheme ofFIG. 6 and may input the 3D information 245 to the neural network 250,to obtain an encoded feature 255. The apparatus may restore 3Dinformation 710 using a decoder neural network 705 corresponding to theneural network 250. In this example, the neural network 250 maycorrespond to an encoding portion of an autoencoder, and the decoderneural network 705 may correspond to a decoding portion of theautoencoder.

The apparatus may determine a loss based on a difference between therestored 3D information 710 and the original 3D information 245, and maytrain the neural network 250 and the decoder neural network 705 based onthe determined loss. Depending on examples, the neural network 250 maybe trained using a generative adversarial network (GAN) that uses anautoencoder as a generator, for example. Also, in an example, asymmetric type auto-encoder having such an encoder and decoder withshared parameters may be implemented. In various examples, the apparatusmay be any or any combination of the apparatuses described herein, andbe configured to implement any combination or all correspondingoperations, in addition as the training of the neural network 250, forexample.

FIG. 7B illustrates an example of a process of reconstructing a 2Dobject into a 3D object. Referring to FIG. 7B, an embedding feature maybe used to determine whether a point in a 3D space is located inside oroutside an object.

An example apparatus may, among other things, obtain a feature 255 of a3D shape for a 2D object included in an input image, using the neuralnetwork 250 that is trained through the above-described process of FIG.7A.

The apparatus may transform the feature 255 to a shape feature y_(i)(n×c) by repeating the feature 255 the same number of times as a numberof points included in a plane 720 in the 3D space. In the shape featurey_(i) (n×c), n denotes the number of points included in the plane 720,and c denotes a length or a dimension of the feature 255. For example,the apparatus may set a sufficiently large number of 3D points{p_(k)}_(k=1) ^(n) in the plane 720 of the world coordinate system, andmay estimate a respective probability of whether each 3D point positionp_(k) is inside or outside the 3D shape. The apparatus may reconstruct a2D object into a 3D object based on the estimated probability.

For example, the apparatus may combine the shape feature y_(i) with each3D point position p_(k) in operation 730, which may be used as an inputof a nonlinear function or a nonlinear neural network, for example, anMLP or a GCN. The apparatus may obtain a probability value o_(k)corresponding to positions p_(k) of 3D points as a result value of thenonlinear function or the nonlinear neural network. In this example, theprobability value o_(k) may correspond to a probability, for example, anoccupancy rate, that a 3D point located at a position p_(k) occupies theinside of the object. The probability value o_(k) may have a valuebetween “0” and “1”. The apparatus may reconstruct a 3D object 740 byrepeating the above-described process with respect to a plurality ofplanes included in the 3D space.

Depending on examples, the apparatus may reconstruct the 3D object 740using a feature map or feature vectors obtained from intermediate layersof the depth estimation network 220 of FIG. 2 together with the feature255 of the 3D shape. For example, 2D input images may be assumed as “N”multi-view images or a sequence image that includes “N” sequential imageframes. Also, the depth estimation network 220 may be assumed as a deepneural network including a plurality of layers. In this example, theapparatus may obtain a feature map or feature vectors for each inputimage in the intermediate layers of the depth estimation network 220.

The apparatus may compress “N” features 255 obtained from each of the“N” multi-view images or “N” image frames into a single feature x′through max pooling. In this example, the feature map or feature vectorsobtained from the intermediate layers of the depth estimation network220, together with the feature 255 of the 3D shape obtained throughfeature embedding, may be applied as inputs of a mapping function, to betransformed into a new shape feature y with integrated 2D informationand 3D information.

To reconstruct the 3D shape, the apparatus may set a sufficiently largenumber of 3D points {p_(k)}_(k=1) ^(n) in the world coordinate systemsuch as the plane 720, and may estimate a probability of whether each 3Dpoint position pk is inside or outside the 3D shape. The apparatus mayreconstruct a 2D object into a 3D object based on the estimatedprobability. The apparatus may estimate a probability that a 3D pointposition pk of a pixel corresponding to the feature 255 is inside the 3Dshape. The apparatus may reconstruct a 2D object into a 3D object basedon the estimated probability.

The apparatus may combine the new shape feature y with each 3D pointposition p_(k), which may be used as an input of a nonlinear function,for example, an MLP or a GCN. The apparatus may obtain a probabilityvalue p_(k) corresponding to a 3D point position p_(k) as a result ofthe nonlinear function. The apparatus may input the positions of all the3D points {p_(k)}_(k=1) ^(n) defined above and the new shape feature yto the nonlinear function, to reconstruct the 3D object 740 fromprobability values of all the 3D points.

FIG. 8 illustrates another example of a feature embedding method. FIG. 8illustrates a process in which an example apparatus, among other things,extracts or embeds a feature including information about a 3D shapethrough operations 810 through 860.

In operation 810, the apparatus may receive input data. The apparatusmay obtain a camera parameter including an intrinsic parameter and anextrinsic parameter in operation 803, and may receive or obtainmulti-view images or a sequence image corresponding to a 2D input imageof a 2D object in operation 806. Although operation 806 is performedafter operation 803 as described above, however, examples are notlimited thereto. For example, operation 803 may be performed afteroperation 806 is performed, or operations 803 and 806 may besimultaneously performed.

In operation 820, the apparatus may estimate depth maps for each imageof either the multi-view images or the sequence image.

In operation 830, the apparatus may incrementally aggregate theestimated depth maps.

In operation 840, the apparatus may unproject the depth mapsincrementally aggregated in operation 830 to a 3D space using the cameraparameters obtained in operation 803.

In operation 850, the apparatus may transform the incrementallyaggregated depth maps into 3D information in a point cloud form throughoperation 840.

In operation 860, the apparatus may extract a feature includinginformation about a 3D shape corresponding to the 2D object included inthe 2D input image, or may embed the information about the 3D shape, byapplying the 3D information obtained in operation 850 to a neuralnetwork.

FIG. 9 illustrates an example of an example apparatus 900. Referring toFIG. 9, the apparatus 900 may include a communication interface 910, aprocessor 930, a memory 950, and a display 970, as non-limitingexamples. The communication interface 910, the processor 930, the memory950 and the display 970 may be connected to each other via acommunication bus 905.

The communication interface 910 may receive or obtain a 2D input image.Also, the communication interface 910 may obtain camera parametersincluding an intrinsic parameter and an extrinsic parametercorresponding to the 2D input image.

The processor 930 may estimate depth maps for each 2D input image. Theprocessor 930 may transform the depth maps into 3D information in apoint cloud form by aggregating the depth maps. The processor 930 maygenerate a feature information about a 3D shape corresponding to a 2Dobject included in a 2D input image by applying the 3D information to aneural network. Depending on examples, the processor 930 may reconstructthe 2D object into a 3D object based on the feature including theinformation about the 3D shape.

Also, the processor 930 may perform one or more or all of theoperations, processes, and/or methods described above with reference toFIGS. 1 through 8. For example, the processor 930 may be ahardware-implemented data processing device having a circuit that isphysically structured to execute desired operations. In an example, theprocessor 930 may implement such desired operations by executing code orinstructions, which through execution by the processor 930 configure theprocessor 930 to implement such desired operations. Such code orinstructions may be stored in the memory 950. The hardware-implementeddata processing device may include, as non-limiting examples, amicroprocessor, a central processing unit (CPU), a graphics processingunit (GPU), a processor core, a multi-core processor, a multiprocessor,an application-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), and a neural processing unit (NPU).

The memory 950 may store the 2D input image received or obtained by thecommunication interface 910 and/or the camera parameters correspondingto the 2D input image. Also, the memory 950 may store the 3D informationin the point cloud form obtained by aggregating the depth maps by theprocessor 930, data generated by applying the 3D information to theneural network by the processor 930, and/or the feature including theinformation about the 3D shape generated by the processor 930. Inaddition, the memory 950 may store the 3D object that is reconstructedfrom the 2D object by the processor 930.

As described above, the memory 950 may store a variety of informationgenerated in a processing process of the processor 930. Also, the memory950 may store a variety of data and programs, execution of which maycontrol the apparatus 900 to implement a variety of other operations.The memory 950 may include, for example, a volatile memory or anon-volatile memory. The memory 950 may include a large-capacity storagemedium such as a hard disk to store a variety of data.

The apparatus 900 is representative of each of, for example, a 3Dprinter, a 3D scanner, an advanced driver-assistance system (ADAS), ahead-up display (HUD), a 3D digital information display (DID), anavigation device, a neuromorphic device, a 3D mobile device, asmartphone, a smart television (TV), a smart vehicle, an Internet ofThings (IoT) device, a medical device, and a measuring device, asnon-limiting examples. For example, the 3D mobile device may be orinclude any of a head-mounted display (HMD), a face-mounted display(FMD), and a device to display any one or any combination of augmentedreality (AR), virtual reality (VR) and mixed reality (MR), in varyingexamples.

For example, when the apparatus 900 is an AR device that is one of the3D mobile devices, the processor 930 may perform encoding with thefeature including the information about the 3D shape corresponding tothe 2D object included in the 2D input image, by applying the 3Dinformation to the neural network.

For example, and as applicable to various embodiments described herein,the processor 930 is further configured to consider additional mappingof input information, such as the RGB value of each pixel to a 3Dobject, e.g., through use of a skip connection. Accordingly, in additionto information based on the depth map, additional image information suchas color or other image information may be additionally mapped to the 3Dobject.

The display 970 may display an output image including the 3D shapereconstructed by the processor 930, as well as the 3D shapereconstructed by the processor 930 with such additionally mapped colorinformation.

The cameras, memories, processors, displays, communication interfaces,communication busses, as well as all other apparatuses, units, modules,devices, systems, and other components described herein with respect toFIGS. 1-9 are implemented by hardware components. Examples of hardwarecomponents that may be used to perform the operations described in thisapplication where appropriate include controllers, sensors, generators,drivers, memories, comparators, arithmetic logic units, adders,subtractors, multipliers, dividers, integrators, and any otherelectronic components configured to perform the operations described inthis application. In other examples, one or more of the hardwarecomponents that perform the operations described in this application areimplemented by computing hardware, for example, by one or moreprocessors or computers. A processor or computer may be implemented byone or more processing elements, such as an array of logic gates, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a programmable logic controller, a field-programmablegate array, a programmable logic array, a microprocessor, or any otherdevice or combination of devices that is configured to respond to andexecute instructions in a defined manner to achieve a desired result. Inone example, a processor or computer includes, or is connected to, oneor more memories storing instructions or software that are executed bythe processor or computer. Hardware components implemented by aprocessor or computer may execute instructions or software, such as anoperating system (OS) and one or more software applications that run onthe OS, to perform the operations described in this application. Thehardware components may also access, manipulate, process, create, andstore data in response to execution of the instructions or software. Forsimplicity, the singular term “processor” or “computer” may be used inthe description of the examples described in this application, but inother examples multiple processors or computers may be used, or aprocessor or computer may include multiple processing elements, ormultiple types of processing elements, or both. For example, a singlehardware component or two or more hardware components may be implementedby a single processor, or two or more processors, or a processor and acontroller. One or more hardware components may be implemented by one ormore processors, or a processor and a controller, and one or more otherhardware components may be implemented by one or more other processors,or another processor and another controller. One or more processors, ora processor and a controller, may implement a single hardware component,or two or more hardware components. A hardware component may have anyone or more of different processing configurations, examples of whichinclude a single processor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 1-9 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software include machine code that is directly executedby the one or more processors or computers, such as machine codeproduced by a compiler. In another example, the instructions or softwareincludes higher-level code that is executed by the one or moreprocessors or computer using an interpreter. The instructions orsoftware may be written using any programming language based on theblock diagrams and the flow charts illustrated in the drawings and thecorresponding descriptions used herein, which disclose algorithms forperforming the operations that are performed by the hardware componentsand the methods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access programmable readonly memory (PROM), electrically erasable programmable read-only memory(EEPROM), random-access memory (RAM), dynamic random access memory(DRAM), static random access memory (SRAM), flash memory, non-volatilememory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-rayor optical disk storage, hard disk drive (HDD), solid state drive (SSD),flash memory, a card type memory such as multimedia card micro or a card(for example, secure digital (SD) or extreme digital (XD)), magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents.

What is claimed is:
 1. A processor-implemented method, the methodcomprising: estimating a depth map for each of plural two-dimensional(2D) input images; transforming the depth maps into three-dimensional(3D) information in a point cloud form based on an aggregation of thedepth maps; and generating an embedded feature by applying the 3Dinformation to a machine learning model, wherein the embedded featureincludes information about a 3D shape corresponding to a 2D object inthe plural 2D input images.
 2. The method of claim 1, wherein thetransforming comprises incrementally aggregating the depth maps, andtransforming a result of the incremental aggregating into the 3Dinformation.
 3. The method of claim 1, wherein the transformingcomprises unprojecting the depth maps to a 3D space using a cameraparameter corresponding to the plural 2D input images.
 4. The method ofclaim 3, wherein the transforming comprises: unprojecting the depth mapsto the 3D space to calculate positions of 3D points that correspond topixels corresponding to the 2D object in the plural 2D input images; andtransforming the depth maps into the 3D information based on thecalculated positions of the 3D points.
 5. The method of claim 1, whereinthe plural 2D input images are multi-view images or a sequence image. 6.The method of claim 5, wherein the plural 2D input images are multi-viewimages, and the 3D information comprises information indicatingrespective surfaces of the 3D shape corresponding to respective views ofthe plural 2D input images.
 7. The method of claim 1, wherein themachine learning model is a neural network, and wherein the generatingof the embedded feature comprises: transforming the 3D information intoinformation of a dimension corresponding to an input layer of the neuralnetwork; and generating the embedded feature by applying the informationof the dimension to the neural network.
 8. The method of claim 7,wherein the transforming of the 3D information into the information ofthe dimension comprises transforming the 3D information into theinformation of the dimension using at least one of a multilayerperceptron (MLP) and a graph convolutional network (GCN).
 9. The methodof claim 1, wherein the machine learning model is a neural network, andwherein the generating of the embedded feature comprises generating theembedded feature to include information about the 3D shape, representingdepth values of all pixels corresponding to the 2D object included inthe plural 2D input images, by applying the 3D information to the neuralnetwork.
 10. The method of claim 1, wherein the embedded feature is in aform of one of a feature map and a feature vector.
 11. The method ofclaim 1, further comprising: reconstructing the 2D object as a 3D objectbased on the embedded feature.
 12. The method of claim 11, wherein thereconstructing of the 2D object as the 3D object comprises: estimating aprobability that a display pixel corresponding to the embedded featureis located inside or outside the 3D shape; and reconstructing the 2Dobject as the 3D object based on the estimated probability.
 13. Themethod of claim 1, wherein the estimating of the depth map for each ofthe plural 2D input images comprises estimating respective depth mapsfrom each of the plural 2D input images using a neural network that istrained to estimate depth.
 14. The method of claim 1, furthercomprising: obtaining respective intrinsic and extrinsic parameterscorresponding to the plural 2D input images, and performing thetransforming dependent on the obtained respective intrinsic andextrinsic parameters.
 15. A non-transitory computer-readable storagemedium storing instructions that, when executed by a processor, causethe processor to perform the method of claim
 1. 16. An apparatus, theapparatus comprising: a processor configured to: estimate a depth mapfor each of plural two-dimensional (2D) input images; transform thedepth maps into three-dimensional (3D) information in a point cloud formbased on an aggregation of the depth maps; and generate an embeddedfeature by applying the 3D information to a machine learning model,wherein the embedded feature includes information about a 3D shapecorresponding to a 2D object included in the plural 2D input images. 17.The apparatus of claim 16, wherein, for the transforming, the processoris configured to incrementally aggregate the depth maps and transform aresult of the incremental aggregation into the 3D information.
 18. Theapparatus of claim 16, wherein, for the transforming, the processor isconfigured to unproject the depth maps to a 3D space using a cameraparameter corresponding to the plural 2D input images.
 19. The apparatusof claim 18, wherein the processor is configured to: unproject the depthmaps to the 3D space to calculate positions of 3D points that correspondto pixels corresponding to the 2D object in the plural 2D input images;and transform the depth maps into the 3D information based on thecalculated positions of the 3D points.
 20. The apparatus of claim 16,wherein the plural 2D input images are multi-view images or a sequenceimage.
 21. The apparatus of claim 20, wherein the plural 2D input imagesare multi-view images, and the 3D information comprises informationindicating respective surfaces of the 3D shape corresponding torespective views of the plural 2D input images.
 22. The apparatus ofclaim 16, wherein the machine learning model is a neural network, andwherein, for the generating of the embedded feature, the processor isconfigured to: transform the 3D information into information of adimension corresponding to an input layer of the neural network; andgenerate the embedded feature by applying the information of thedimension to the neural network.
 23. The apparatus of claim 22, wherein,for the transforming of the 3D information into the information of thedimension, the processor is configured to transform the 3D informationinto the information of the dimension using at least one of a multilayerperceptron (MLP) and a graph convolutional network (GCN).
 24. Theapparatus of claim 16, wherein the machine learning model is a neuralnetwork, and wherein, for the generating of the embedded feature, theprocessor is configured to generate the embedded feature to includeinformation about the 3D shape, representing depth values of all pixelscorresponding to the 2D object included in the plural 2D input images,by applying the 3D information to the neural network.
 25. The apparatusof claim 16, wherein the embedded feature is in a form of one of afeature map and a feature vector.
 26. The apparatus of claim 16, whereinthe processor is configured to reconstruct the 2D object as a 3D objectbased on the embedded feature.
 27. The apparatus of claim 26, wherein,for the reconstructing of the 2D object as the 3D object, the processoris configured to: estimate a probability that a display pixelcorresponding to the embedded feature is located inside or outside the3D shape; and reconstruct the 2D object as the 3D object based on theestimated probability.
 28. The apparatus of claim 16, wherein, for theestimating of the depth map for each of the plural 2D input images, theprocessor is configured to estimate respective depth maps from each ofthe plural 2D input images using a neural network that is trained toestimate depth.
 29. The apparatus of claim 16, further comprising aninterface configured to obtain the plural 2D input images, wherein theprocessor is configured perform the transforming of the depth mapsdependent on respective intrinsic and extrinsic parameters correspondingto the obtaining of the plural 2D input images.
 30. The apparatus ofclaim 29, wherein the interface is a communication interface.
 31. Theapparatus of claim 16, wherein the apparatus is one of a 3D printer, a3D scanner, an advanced driver-assistance system (ADAS), a head-updisplay (HUD), a 3D digital information display (DID), a navigationdevice, a neuromorphic device, a 3D mobile device, a smartphone, a smarttelevision (TV), a smart vehicle, an Internet of Things (IoT) device, amedical device, and a measuring device.
 32. The apparatus of claim 16,wherein the processor is further configured to reconstruct the 2D objectas a 3D object, based on the embedded feature, and wherein the apparatusis an augmented reality apparatus and further comprises a displaycontrolled to display the reconstructed 2D object.
 33. The apparatus ofclaim 16, further comprising a memory, and the processor is furtherconfigured to store the embedded feature in the memory.
 34. An augmentedreality (AR) apparatus comprising: a communication interface configuredto receive plural two-dimensional (2D) input images comprisingmulti-view images or a sequence image; a processor configured to:estimate respective depth maps for the plural 2D input images; transformthe respective depth maps into three-dimensional (3D) information in apoint cloud form based on an aggregation of the respective depth maps;perform encoding of a feature to include information about a 3D shapecorresponding to a 2D object included in the plural 2D input images byapplying the 3D information to a neural network; and reconstruct the 3Dshape corresponding to the 2D object based on the feature; and a displayconfigured to display an output image comprising the 3D shape.
 35. Theapparatus of claim 34, further comprising a memory, and the processor isfurther configured to store the feature in the memory.
 36. An apparatus,the apparatus comprising: a memory storing an embedded feature thatincludes information about a 3D shape corresponding to a 2D object; adisplay; and a processor configured to reconstruct the 2D object as a 3Dobject based on the embedded feature and control display of thereconstructed 2D object, wherein the embedded feature is a featurereflecting having been generated based on depth maps for each of pluraltwo-dimensional (2D) images, a transformation of the depth maps intothree-dimensional (3D) information based on an aggregation of the depthmaps, and by an application of the 3D information to a neural network.